Статті в журналах: "Representation of words"

1

Daya, Shari. "Words and worlds: textual representation and new materialism." cultural geographies 26, no. 3 (February 28, 2019): 361–77. http://dx.doi.org/10.1177/1474474019832356.

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2

Chi, Jinjin, Jihong Ouyang, Changchun Li, Xueyang Dong, Ximing Li, and Xinhua Wang. "Topic representation: Finding more representative words in topic models." Pattern Recognition Letters 123 (May 2019): 53–60. http://dx.doi.org/10.1016/j.patrec.2019.01.018.

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3

Ben-Yami, Hanoch. "Word, Sign and Representation in Descartes." Journal of Early Modern Studies 10, no. 1 (2021): 29–46. http://dx.doi.org/10.5840/jems20211012.

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In the first chapter of his The World, Descartes compares light to words and discusses signs and ideas. This made scholars read into that passage our views of language as a representational medium and consider it Descartes’ model for representation in perception. I show, by contrast, that Descartes does not ascribe there any representational role to language; that to be a sign is for him to have a kind of causal role; and that he is concerned there only with the cause’s lack of resemblance to its effect, not with the representation’s lack of resemblance to what it represents. I support this interpretation by comparisons with other places in Descartes’ corpus and with earlier authors, Descartes’ likely sources. This interpretation may shed light both on Descartes’ understanding of the functioning of language and on the development of his theory of representation in perception.

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4

Ramanisa, Hartiwi, Khairudin Khairudin, and Syukma Netti. "ANALISIS KEMAMPUAN REPRESENTASI MATEMATIS SISWA." Jurnal Magister Pendidikan Matematika (JUMADIKA) 2, no. 1 (August 20, 2020): 34–38. http://dx.doi.org/10.30598/jumadikavol2iss1year2020page34-38.

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The ability Mathematical representation is very important for students to have, because it can facilitate students in solving mathematical problems. This study aims to describe the ability of mathematical representation of class VII students of SMP Negeri 14 Padang. The results showed that 82.7% of students used visual representations, 38% of students used representations of Equation or mathematical expressions and 23.3% of students used representations of words or written texts. The highest indicator of visual representation is to draw geometric figures to clarify the problem and facilitate its solution, while the lowest indicator of the representation of words or written text is to write the steps to solve mathematical problems with words

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5

Fédou, Jean-Marc, and Gabriele Fici. "Vertical representation of C∞-words." Theoretical Computer Science 565 (February 2015): 90–101. http://dx.doi.org/10.1016/j.tcs.2014.11.011.

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6

Klein, Devorah E., and Gregory L. Murphy. "The Representation of Polysemous Words." Journal of Memory and Language 45, no. 2 (August 2001): 259–82. http://dx.doi.org/10.1006/jmla.2001.2779.

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7

Andres, Michael, Etienne Olivier, and Arnaud Badets. "Actions, Words, and Numbers." Current Directions in Psychological Science 17, no. 5 (October 2008): 313–17. http://dx.doi.org/10.1111/j.1467-8721.2008.00597.x.

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Recent findings in neuroscience challenge the view that the motor system is exclusively dedicated to the control of actions, and it has been suggested that it may contribute critically to conceptual processes such as those involved in language and number representation. The aim of this review is to address this issue by illustrating some interactions between the motor system and the processing of words and numbers. First, we detail functional brain imaging studies suggesting that motor circuits may be recruited to represent the meaning of action-related words. Second, we summarize a series of experiments demonstrating some interference between the size of grip used to grasp objects and the magnitude processing of words or numbers. Third, we report data suggestive of a common representation of numbers and finger movements in the adult brain, a possible trace of the finger-counting strategies used in childhood. Altogether, these studies indicate that the motor system interacts with several aspects of word and number representations. Future research should determine whether these findings reflect a causal role of the motor system in the organization of semantic knowledge.

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8

Clahsen, Harald, and Yu Ikemoto. "The mental representation of derived words." Mental Lexicon 7, no. 2 (December 7, 2012): 147–82. http://dx.doi.org/10.1075/ml.7.2.02cla.

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Deadjectival nominals with –sa and –mi in Japanese are both phonologically transparent and morphologically decomposable. However, whilst –sa essentially serves to form nouns out of adjectives, –mi forms function as semantic labels with specific meanings. We examined –sa and –mi nominals in three experiments, an eye-movement experiment presenting –sa and –mi forms in sentence contexts and in two word recognition experiments using (primed and unprimed) lexical decision, to investigate the nature of their form-level representations. Whilst the word recognition experiments produced the same pattern of results for –sa and –mi forms, the eye-movement experiment demonstrated clear differences: –mi forms elicited longer reading times compared to –sa forms, except when the particular meanings of –mi forms were contextually licensed. These results show how different semantic properties affect the performance of derived words that have the same type of word level representation.

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9

Zuo, Yuanyuan, and Bo Zhang. "Sparse Based Image Classification With Bag-of-Visual-Words Representations." International Journal of Software Science and Computational Intelligence 3, no. 1 (January 2011): 1–15. http://dx.doi.org/10.4018/jssci.2011010101.

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The sparse representation based classification algorithm has been used to solve the problem of human face recognition, but the image database is restricted to human frontal faces with only slight illumination and expression changes. This paper applies the sparse representation based algorithm to the problem of generic image classification, with a certain degree of intra-class variations and background clutter. Experiments are conducted with the sparse representation based algorithm and Support Vector Machine (SVM) classifiers on 25 object categories selected from the Caltech101 dataset. Experimental results show that without the time-consuming parameter optimization, the sparse representation based algorithm achieves comparable performance with SVM. The experiments also demonstrate that the algorithm is robust to a certain degree of background clutter and intra-class variations with the bag-of-visual-words representations. The sparse representation based algorithm can also be applied to generic image classification task when the appropriate image feature is used.

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10

Ganguly, Debasis. "Learning variable-length representation of words." Pattern Recognition 103 (July 2020): 107306. http://dx.doi.org/10.1016/j.patcog.2020.107306.

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11

Prunet, Jean-François, Renée Béland, and Ali Idrissi. "The Mental Representation of Semitic Words." Linguistic Inquiry 31, no. 4 (October 2000): 609–48. http://dx.doi.org/10.1162/002438900554497.

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This article is concerned with external evidence bearing on the nature of the units stored in the mental lexicons of speakers of Semitic languages. On the basis of aphasic metathesis errors we collected in a single case study, we suggest that roots can be accessed as independent morphological units. We review documented language games and slips of the tongue that lead to the same conclusion. We also discuss evidence for the morphemic status of templates from aphasic errors, language games, and slips of the tongue. We conclude that the available external evidence is best accounted for within a morpheme-based theory of morphology that forms words by combining roots and templates.

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12

Burani, Cristina, and Alfonso Caramazza. "Representation and processing of derived words." Language and Cognitive Processes 2, no. 3-4 (August 1987): 217–27. http://dx.doi.org/10.1080/01690968708406932.

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13

Schwartz, Ana I., Li-Hao Yeh, and Moira P. Shaw. "Lexical representation of second language words." Mental Lexicon 3, no. 3 (December 10, 2008): 309–24. http://dx.doi.org/10.1075/ml.3.3.04sch.

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The goal of the present study was to examine whether cross-language activation of a bilingual’s native language influences the processing of lexical ambiguity within a second language. Highly proficient Spanish-English bilinguals performed a semantic verification task in which sentence frames were followed by the presentation of the final word of the sentence (the prime word). Participants then decided whether a follow-up target word was related to the meaning of the sentence. On critical trials the sentences ended in a semantically ambiguous word that was either a cognate with Spanish (e.g., novel), or a noncognate control matched on frequency and length (e.g., fast). The preceding sentence context biased the subordinate meaning (e.g., new; refrain from eating) and targets were related to the irrelevant, dominant meaning (e.g., BOOK; SPEED). Mean reaction times and error rates were greater when the prime words were ambiguous cognates than when they were ambiguous noncognates. This suggests that the semantic representations from the native language were coactivated and increased the lexical competition from the shared, dominant meaning. Implications for second language vocabulary acquisition and current models of reading are discussed.

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14

Maroli, John A. "Representation of tree permutations by words." Proceedings of the American Mathematical Society 110, no. 4 (April 1, 1990): 859. http://dx.doi.org/10.1090/s0002-9939-1990-1037214-x.

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15

Lu, Guangquan, and Jihong Huang. "Learning Representation From Concurrence-Words Graph For Aspect Sentiment Classification." Computer Journal 64, no. 7 (July 2021): 1069–79. http://dx.doi.org/10.1093/comjnl/bxab104.

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Abstract Aspect sentiment classification is an important research topic in natural language processing and computational linguistics, assisting in automatically review analysis and emotional tendency judgement. Different from extant methods that focus on text sequence representations, this paper presents a network framework to learn representation from concurrence-words relation graph (LRCWG), so as to improve the Macro-F1 and accuracy. The LRCWG first employs the multi-head attention mechanism to capture the sentiment representation from the sentences which can learn the importance of text sequence representation. And then, it leverages the priori sentiment dictionary information to construct the concurrence relations of sentiment words with Graph Convolution Network (GCN). This assists in that the learnt context representation can keep both the semantics integrity and the features of sentiment concurrence-words relations. The designed algorithm is experimentally evaluated with all the five benchmark datasets and demonstrated that the proposed aspect sentiment classification can significantly improve the prediction performance of learning task.

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16

Vitevitch, Michael S., and Paul A. Luce. "When Words Compete: Levels of Processing in Perception of Spoken Words." Psychological Science 9, no. 4 (July 1998): 325–29. http://dx.doi.org/10.1111/1467-9280.00064.

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Current theories of spoken-word recognition posit two levels of representation and process: lexical and sublexical. By manipulating probabilistic phonotactics and similarity-neighborhood density, we attempted to determine if these two levels of representation have dissociable effects on processing. Whereas probabilistic phonotactics have been associated with facilitatory effects on recognition, increases in similarity-neighborhood density typically result in inhibitory effects on recognition arising from lexical competition. Our results demonstrated that when the lexical level is invoked using real words, competitive effects of neighborhood density are observed. However, when strong lexical effects are removed by the use of nonsense word stimuli, facilitatory effects of phonotactics emerge. These results are consistent with a two-level framework of process and representation embodied in certain current models of spoken-word recognition.

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17

CARPI, ARTURO. "ON REPRESENTATION SYSTEMS." International Journal of Algebra and Computation 04, no. 04 (December 1994): 657–90. http://dx.doi.org/10.1142/s0218196794000191.

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We introduce the notion of a representation system: it can be viewed as a generalization of a numeration system in an integer base by means of which certain words on a finite alphabet can be represented. We show that under suitable hypotheses, concatenation of words is represented by a right-synchronized rational relation. We study languages which are represented by rational sets (this is the case, e. g., of the languages of binary overlap-free words, of partially abelian square-free words on three letters, and of a large class of PD0L languages). Several results on these languages are obtained, concerning density, prolongability, pattern-freeness.

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18

Pylkkänen, Liina, Rodolfo Llinás, and Gregory L. Murphy. "The Representation of Polysemy: MEG Evidence." Journal of Cognitive Neuroscience 18, no. 1 (January 1, 2006): 97–109. http://dx.doi.org/10.1162/089892906775250003.

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Most words in natural language are polysemous, that is, they can be used in more than one way. For example, paper can be used to refer to a substance made out of wood pulp or to a daily publication printed on that substance. Although virtually every sentence contains polysemy, there is little agreement as to how polysemy is represented in the mental lexicon. Do different uses of polysemous words involve access to a single representation or do our minds store distinct representations for each different sense? Here we investigated priming between senses with a combination of behavioral and magnetoencephalographic measures in order to test whether different senses of the same word involve identity or mere formal and semantic similarity. Our results show that polysemy effects are clearly distinct from similarity effects bilaterally. In the left hemisphere, sense-relatedness elicited shorter latencies of the M350 source, which has been hypothesized to index lexical activation. Concurrent activity in the right hemisphere, on the other hand, peaked later for sense-related than for unrelated target stimuli, suggesting competition between related senses. The obtained pattern of results supports models in which the representation of polysemy involves both representational identity and difference: Related senses connect to same abstract lexical representation, but are distinctly listed within that representation.

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19

Strother, Lars, Alexandra M. Coros, and Tutis Vilis. "Visual Cortical Representation of Whole Words and Hemifield-split Word Parts." Journal of Cognitive Neuroscience 28, no. 2 (February 2016): 252–60. http://dx.doi.org/10.1162/jocn_a_00900.

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Reading requires the neural integration of visual word form information that is split between our retinal hemifields. We examined multiple visual cortical areas involved in this process by measuring fMRI responses while observers viewed words that changed or repeated in one or both hemifields. We were specifically interested in identifying brain areas that exhibit decreased fMRI responses as a result of repeated versus changing visual word form information in each visual hemifield. Our method yielded highly significant effects of word repetition in a previously reported visual word form area (VWFA) in occipitotemporal cortex, which represents hemifield-split words as whole units. We also identified a more posterior occipital word form area (OWFA), which represents word form information in the right and left hemifields independently and is thus both functionally and anatomically distinct from the VWFA. Both the VWFA and the OWFA were left-lateralized in our study and strikingly symmetric in anatomical location relative to known face-selective visual cortical areas in the right hemisphere. Our findings are consistent with the observation that category-selective visual areas come in pairs and support the view that neural mechanisms in left visual cortex—especially those that evolved to support the visual processing of faces—are developmentally malleable and become incorporated into a left-lateralized visual word form network that supports rapid word recognition and reading.

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20

DURRANT, SAMANTHA, CLAIRE DELLE LUCHE, ALLEGRA CATTANI, and CAROLINE FLOCCIA. "Monodialectal and multidialectal infants’ representation of familiar words." Journal of Child Language 42, no. 2 (March 21, 2014): 447–65. http://dx.doi.org/10.1017/s0305000914000063.

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ABSTRACTMonolingual infants are typically studied as a homogenous group and compared to bilingual infants. This study looks further into two subgroups of monolingual infants, monodialectal and multidialectal, to identify the effects of dialect-related variation on the phonological representation of words. Using an Intermodal Preferential Looking task, the detection of mispronunciations in familiar words was compared in infants aged 1;8 exposed to consistent (monodialectal) or variable (multidialectal) pronunciations of words in their daily input. Only monodialectal infants detected the mispronunciations whereas multidialectal infants looked longer at the target following naming whether the label was correctly produced or not. This suggests that variable phonological input in the form of dialect variation impacts the degree of specificity of lexical representations in early infancy.

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21

Farhangi, Mohammad Mehdi, Mohsen Soryani, and Mahmood Fathy. "Informative visual words construction to improve bag of words image representation." IET Image Processing 8, no. 5 (May 1, 2014): 310–18. http://dx.doi.org/10.1049/iet-ipr.2013.0449.

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22

Von Eckardt, Barbara, and Mary C. Potter. "Clauses and the semantic representation of words." Memory & Cognition 13, no. 4 (July 1985): 371–76. http://dx.doi.org/10.3758/bf03202505.

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23

Cordeschi, Roberto. "A few words on representation and meaning." International Studies in the Philosophy of Science 6, no. 1 (January 1992): 19–21. http://dx.doi.org/10.1080/02698599208573405.

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24

Nizaruddin, Nizaruddin, Muhtarom Muhtarom, and Yanuar Hery Murtianto. "EXPLORING OF MULTI MATHEMATICAL REPRESENTATION CAPABILITY IN PROBLEM SOLVING ON SENIOR HIGH SCHOOL STUDENTS." Problems of Education in the 21st Century 75, no. 6 (December 15, 2017): 591–98. http://dx.doi.org/10.33225/pec/17.75.591.

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The students’ multi-mathematical representation capability in problem solving is very important and interesting to discuss, specifically for problems in the two-variable linear equation system. Data was collected from 48 students using written tests and in-depth interviews with selected participants. The research findings showed that few students are using three representations namely symbolic - verbal - table representation, and symbolic representation, however most of the students are using three representations namely symbolic - verbal - images representation, and two representations namely symbolic – verbal representations, and the rest used symbolic representation. In the use of verbal representation, some students had difficulty composing words and all students encountered difficulties in the translational process from symbolic representation and verbal representation to other types of representation. The ability to understand concepts and relationships between mathematical concepts was found to be a necessary condition for the achievement of multi-mathematical representation capability. It is therefore recommended that teachers use a variety of different types of representation, such as verbal, tables and images, to enhance students' understanding of the material. Keywords: multiple representations, problem solving, two-variable linear equation system.

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Nizaruddin, Nizaruddin, Muhtarom Muhtarom, and Yanuar Hery Murtianto. "EXPLORING OF MULTI MATHEMATICAL REPRESENTATION CAPABILITY IN PROBLEM SOLVING ON SENIOR HIGH SCHOOL STUDENTS." Problems of Education in the 21st Century 75, no. 6 (December 15, 2017): 591–98. http://dx.doi.org/10.33225/17.75.591.

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The students’ multi-mathematical representation capability in problem solving is very important and interesting to discuss, specifically for problems in the two-variable linear equation system. Data was collected from 48 students using written tests and in-depth interviews with selected participants. The research findings showed that few students are using three representations namely symbolic - verbal - table representation, and symbolic representation, however most of the students are using three representations namely symbolic - verbal - images representation, and two representations namely symbolic – verbal representations, and the rest used symbolic representation. In the use of verbal representation, some students had difficulty composing words and all students encountered difficulties in the translational process from symbolic representation and verbal representation to other types of representation. The ability to understand concepts and relationships between mathematical concepts was found to be a necessary condition for the achievement of multi-mathematical representation capability. It is therefore recommended that teachers use a variety of different types of representation, such as verbal, tables and images, to enhance students' understanding of the material. Keywords: multiple representations, problem solving, two-variable linear equation system.

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26

Wang, Chunlei, and Na Li. "Bilingual Lexical Representation and Its Access." Journal of Critical Studies in Language and Literature 1, no. 4 (October 18, 2020): 22–27. http://dx.doi.org/10.46809/jcsll.v1i4.39.

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The purpose of this paper is to explore the terminology, concepts and access to bilingual lexical representation. The core problem of linguistic cognitive structure is linguistic representation which is the reflection of individual psychology on linguistic knowledge. In terms of linguistic representation, the research and experiments on the evidence of lexical representation in modern psycholinguistic period are reviewed. Psycholinguistic studies attempt to apply elucidate language theories and model systems to operate and interpret representational data. We recognize that the use of the concept of lexical representation may contribute to the search for "psychological grammar" .Moreover, we present the original intention of studying bilingual representation and three approaches of the bilingual lexical representation: lexical meaning, direct representation of reality, functional representations. Our focuses are models of lexical access, variables that influence lexical access and appraising models of lexical access. Then we represent models of lexical access, which are influenced by variety of factors, including the frequency of a word, its phonological structure, its syntactic category, its morphological structure, the presence of semantically related words, and the existence of alternative meaning of the word. It is concluded that bilingual lexical representation access is influenced by a variety of factors.

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27

Perry, Lynn K., Molly L. Lewis, and Gary Lupyan. "Shaping semantic networks with transcranial direct current stimulation." Quarterly Journal of Experimental Psychology 73, no. 11 (July 6, 2020): 1891–907. http://dx.doi.org/10.1177/1747021820936207.

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Images depict specific objects (e.g., a specific dog), yet are named with categorical labels (e.g., “dog”). We examined how semantic representations activated by images may be influenced by implicit labelling. Participants saw images of familiar objects and generated words associated with each image while undergoing transcranial direct current stimulation over the posterior superior temporal gyrus. Additional participants judged how representative generated associates were of the picture category and guessed the category based on the associates. Anodal stimulation was predicted to up-regulate labelling and thereby increase the extent to which participants produced associate that were more representative of the pictured category. Associates generated by anodally stimulated subjects were found to be more representative and enabled more accurate guessing of the category from which they were generated. The general pattern of results was replicated in a follow-up study using words rather than picture cues. Together these results suggest labelling may help stabilise semantic representations, leading to more robust representation of category-relevant information.

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Taft, Marcus, and Sam Ardasinski. "Obligatory decomposition in reading prefixed words." Mental Lexicon 1, no. 2 (August 30, 2006): 183–99. http://dx.doi.org/10.1075/ml.1.2.02taf.

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The reported experiment examines the impact of stem frequency on lexical decision responses to prefixed words. Both when the nonword distractors had nonsense stems (e.g., recodge) and real-word stems (e.g., relaugh), words with high frequency stems (e.g., unreal) were recognized more quickly than words with low frequency stems (e.g., refuel) when matched on surface frequency. This was taken as evidence that a whole-word representation exists for prefixed words, but that activation of this representation is always mediated by a representation of the stem, unlike the claims of a Dual Pathways model.

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Kim, Han Kyul, Hyunjoong Kim, and Sungzoon Cho. "Bag-of-concepts: Comprehending document representation through clustering words in distributed representation." Neurocomputing 266 (November 2017): 336–52. http://dx.doi.org/10.1016/j.neucom.2017.05.046.

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30

Kumari, R. K., R. Arulprakasam, R. Perumal, and V. R. Dare. "CYCLIC PARTIAL WORDS." Advances in Mathematics: Scientific Journal 9, no. 11 (November 3, 2020): 9219–30. http://dx.doi.org/10.37418/amsj.9.11.27.

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Partial words are linear words with holes. Cyclic words are derived from linear words by linking its first letter after the last one. Both partial words and cyclic words have wide applications in DNA sequencing. In this paper we introduce cyclic partial words and discuss their periodicity and certain properties. We also establish representation of a cyclic partial word using trees.

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31

Holyfield, Christine. "Comparative Effects of Picture Symbol With Paired Text and Text-Only Augmentative and Alternative Communication Representations on Communication From Children With Autism Spectrum Disorder." American Journal of Speech-Language Pathology 30, no. 2 (March 26, 2021): 584–97. http://dx.doi.org/10.1044/2020_ajslp-20-00099.

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Purpose Technology features that maximize communicative benefit while minimizing learning demands must be identified and prioritized to amplify the efficiency and effectiveness of augmentative and alternative communication (AAC) intervention. Picture symbols with paired text are a common representation feature in AAC systems for children with autism spectrum disorder (ASD) who are preliterate, yet little research about their comparative benefit exists. Method Four school-age children with ASD and limited speech who were preliterate participated in two single-subject studies. In one study, communication of high imageability words (e.g., nouns) on an AAC app during a book-reading activity was compared across two representation conditions: picture symbols with paired text and text only. In the second study, communication of low imageability words (e.g., verbs) was compared. Both studies had baseline, intervention, generalization, and maintenance phases. Results Prior to intervention, participants communicated across both representation conditions at low rates except two participants who were relatively successful using picture symbol with paired text representations of high imageability words. In response to intervention, all participants demonstrated increases in communication across representation conditions and maintained the increases. Participants demonstrated generalization in the text-only representation condition. Conclusions Children with ASD who were preliterate acquired communication at comparable rates regardless of whether an AAC app utilized picture symbol with paired text or text-only representation. Therefore, while larger scale research is needed, clinicians and technology developers could consider increasing the use of text in AAC representation given the inherent value associated with learning to recognize written words. Supplemental Material https://doi.org/10.23641/asha.13661357

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Bordag, Denisa, Amit Kirschenbaum, Maria Rogahn, Andreas Opitz, and Erwin Tschirner. "SEMANTIC REPRESENTATION OF NEWLY LEARNED L2 WORDS AND THEIR INTEGRATION IN THE L2 LEXICON." Studies in Second Language Acquisition 39, no. 1 (March 15, 2016): 197–212. http://dx.doi.org/10.1017/s0272263116000048.

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The present semantic priming study explores the integration of newly learnt L2 German words into the L2 semantic network of German advanced learners. It provides additional evidence in support of earlier findings reporting semantic inhibition effects for emergent representations. An inhibitory mechanism is proposed that temporarily decreases the resting levels of the representations with which the new representation is linked and thus enables its selection despite its low resting level.

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Han, Tingting, Yuankai Qi, and Suguo Zhu. "A Continuous Semantic Embedding Method for Video Compact Representation." Electronics 10, no. 24 (December 14, 2021): 3106. http://dx.doi.org/10.3390/electronics10243106.

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Video compact representation aims to obtain a representation that could reflect the kernel mode of video content and concisely describe the video. As most information in complex videos is either noisy or redundant, some researchers have instead focused on long-term video semantics. Recent video compact representation methods heavily rely on the segmentation accuracy of video semantics. In this paper, we propose a novel framework to address these challenges. Specifically, we designed a novel continuous video semantic embedding model to learn the actual distribution of video words. First, an embedding model based on the continuous bag of words method is proposed to learn the video embeddings, integrated with a well-designed discriminative negative sampling approach, which helps emphasize the convincing clips in the embedding while weakening the influence of the confusing ones. Second, an aggregated distribution pooling method is proposed to capture the semantic distribution of kernel modes in videos. Finally, our well-trained model can generate compact video representations by direct inference, which provides our model with a better generalization ability compared with those of previous methods. We performed extensive experiments on event detection and the mining of representative event parts. Experiments on TRECVID MED11 and CCV datasets demonstrated the effectiveness of our method. Our method could capture the semantic distribution of kernel modes in videos and shows powerful potential to discover and better describe complex video patterns.

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Whitney, Carol, Daisy Bertrand, and Jonathan Grainger. "On Coding the Position of Letters in Words." Experimental Psychology 59, no. 2 (November 1, 2012): 109–14. http://dx.doi.org/10.1027/1618-3169/a000132.

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Open-bigram and spatial-coding schemes provide different accounts of how letter position is encoded by the brain during visual word recognition. Open-bigram coding involves an explicit representation of order based on letter pairs, while spatial coding involves a comparison function operating over representations of individual letters. We identify a set of priming conditions (subset primes and reversed interior primes) for which the two types of coding schemes give opposing predictions, hence providing the opportunity for strong scientific inference. Experimental results are consistent with the open-bigram account, and inconsistent with the spatial-coding scheme.

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Syafitri, Aulia, Roseli Theis, and Dewi Iriani. "ANALISIS KESULITAN KEMAMPUAN REPRESENTASI MATEMATIS SISWA EKSTROVERT DALAM MENYELESAIKAN SOAL MATEMATIKA PADA MATERI ALJABAR." Absis: Mathematics Education Journal 3, no. 1 (March 20, 2021): 16. http://dx.doi.org/10.32585/absis.v3i1.1382.

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The objective of this study is to analyze the difficulty of representation ability Mathematical extrovert students in class VII-D in solving math problems on algebraic material, as well as knowing the factors that cause difficulties experienced by students in meet the indicators of mathematical representation ability. The ability of mathematical representations is measured based on Its aspects include visual representation aspects, expression representation aspects mathematics, and aspects of the representation of words or written text. This is qualitative research using a descriptive approach. This is carried out at SMP Negeri 22 Jambi with 4 students from class VII-D. The results showed that SE1, SE2, SE3 and SE4 are students with extrovert personalities do not have difficulty in the visual aspect. On aspects of representation of mathematical equations or expressions of Students SE1, SE3, and SE4 having difficulty making mathematical models or equations. On that aspect word or written text students SE2, SE3, and SE4 have difficulty when determine what steps will be taken to solve the problem mathematical. Factors causing difficulties in the mathematical representation ability are on visual aspects, representational aspects of mathematical expressions, and aspects of word representation or written text is a non-cognitive learning factor

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Montefinese, Maria. "Semantic representation of abstract and concrete words: a minireview of neural evidence." Journal of Neurophysiology 121, no. 5 (May 1, 2019): 1585–87. http://dx.doi.org/10.1152/jn.00065.2019.

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Evidence from both behavioral and neuropsychological studies suggest that different types of organizational principles govern semantic representations of abstract and concrete words. The reviewed neuroimaging studies provide new evidence about the role of brain areas of the semantic network involved in the encoding of some types of information during processing of abstract and concrete concepts, better characterizing the neural underpinnings and the organizational principles of semantic representation of these types of word.

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Govender, Divina, and Jules-Raymond Tapamo. "Spatio-Temporal Scale Coded Bag-of-Words." Sensors 20, no. 21 (November 9, 2020): 6380. http://dx.doi.org/10.3390/s20216380.

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The Bag-of-Words (BoW) framework has been widely used in action recognition tasks due to its compact and efficient feature representation. Various modifications have been made to this framework to increase its classification power. This often results in an increased complexity and reduced efficiency. Inspired by the success of image-based scale coded BoW representations, we propose a spatio-temporal scale coded BoW (SC-BoW) for video-based recognition. This involves encoding extracted multi-scale information into BoW representations by partitioning spatio-temporal features into sub-groups based on the spatial scale from which they were extracted. We evaluate SC-BoW in two experimental setups. We first present a general pipeline to perform real-time action recognition with SC-BoW. Secondly, we apply SC-BoW onto the popular Dense Trajectory feature set. Results showed SC-BoW representations to successfully improve performance by 2–7% with low added computational cost. Notably, SC-BoW on Dense Trajectories outperformed more complex deep learning approaches. Thus, scale coding is a low-cost and low-level encoding scheme that increases classification power of the standard BoW without compromising efficiency.

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BRZOZOWSKI, JANUSZ, and HELMUT JÜRGENSEN. "REPRESENTATION OF SEMIAUTOMATA BY CANONICAL WORDS AND EQUIVALENCES." International Journal of Foundations of Computer Science 16, no. 05 (October 2005): 831–50. http://dx.doi.org/10.1142/s0129054105003327.

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We study a novel representation of semiautomata, which is motivated by the method of trace-assertion specifications of software modules. Each state of the semiautomaton is represented by an arbitrary word leading to that state, the canonical word. The transitions of the semiautomaton give rise to a right congruence, the state-equivalence, on the set of input words of the semiautomaton: two words are state-equivalent if and only if they lead to the same state. We present a simple algorithm for finding a set of generators for state-equivalence. Directly from this set of generators, we construct a confluent prefix-rewriting system which permits us to transform any word to its canonical representative. In general, the rewriting system may allow infinite derivations. To address this issue, we impose the condition of prefix-continuity on the set of canonical words. A set is prefix-continuous if, whenever a word w and a prefix u of w are in the set, then all the prefixes of w longer than u are also in the set. Prefix-continuous sets include prefix-free and prefix-closed sets as special cases. We prove that the rewriting system is Noetherian if and only if the set of canonical words is prefix-continuous. Furthermore, if the set of canonical words is prefix-continuous, then the set of rewriting rules is irredundant. We show that each prefix-continuous canonical set corresponds to a spanning forest of the semiautomaton.

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Broersma, Mirjam. "Lexical representation of perceptually difficult second-language words." Journal of the Acoustical Society of America 132, no. 3 (September 2012): 2053. http://dx.doi.org/10.1121/1.4755558.

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Kousta, Stavroula-Thaleia, Gabriella Vigliocco, David P. Vinson, Mark Andrews, and Elena Del Campo. "The representation of abstract words: Why emotion matters." Journal of Experimental Psychology: General 140, no. 1 (2011): 14–34. http://dx.doi.org/10.1037/a0021446.

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Zhao, Rui, and Kezhi Mao. "Fuzzy Bag-of-Words Model for Document Representation." IEEE Transactions on Fuzzy Systems 26, no. 2 (April 2018): 794–804. http://dx.doi.org/10.1109/tfuzz.2017.2690222.

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Johnson-Laird, P. N. "The mental representation of the meaning of words." Cognition 25, no. 1-2 (March 1987): 189–211. http://dx.doi.org/10.1016/0010-0277(87)90009-6.

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Kesorn, Kraisak, Sutasinee Chimlek, Stefan Poslad, and Punpiti Piamsa-nga. "Visual content representation using semantically similar visual words." Expert Systems with Applications 38, no. 9 (September 2011): 11472–81. http://dx.doi.org/10.1016/j.eswa.2011.03.021.

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Ljubešić, Nikola. "‟Deep lexicography” – Fad or Opportunity?" Rasprave Instituta za hrvatski jezik i jezikoslovlje 46, no. 2 (October 30, 2020): 839–52. http://dx.doi.org/10.31724/rihjj.46.2.21.

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In recent years, we are witnessing staggering improvements in various semantic data processing tasks due to the developments in the area of deep learning, ranging from image and video processing to speech processing, and natural language understanding. In this paper, we discuss the opportunities and challenges that these developments pose for the area of electronic lexicography. We primarily focus on the concept of representation learning of the basic elements of language, namely words, and the applicability of these word representations to lexicography. We first discuss well-known approaches to learning static representations of words, the so-called word embeddings, and their usage in lexicography-related tasks such as semantic shift detection, and cross-lingual prediction of lexical features such as concreteness and imageability. We wrap up the paper with the most recent developments in the area of word representation learning in form of learning dynamic, context-aware representations of words, showcasing some dynamic word embedding examples, and discussing improvements on lexicography-relevant tasks of word sense disambiguation and word sense induction.

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Lu, Wenpeng, Rui Yu, Shoujin Wang, Can Wang, Ping Jian, and Heyan Huang. "Sentence Semantic Matching Based on 3D CNN for Human–Robot Language Interaction." ACM Transactions on Internet Technology 21, no. 4 (July 16, 2021): 1–24. http://dx.doi.org/10.1145/3450520.

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The development of cognitive robotics brings an attractive scenario where humans and robots cooperate to accomplish specific tasks. To facilitate this scenario, cognitive robots are expected to have the ability to interact with humans with natural language, which depends on natural language understanding ( NLU ) technologies. As one core task in NLU, sentence semantic matching ( SSM ) has widely existed in various interaction scenarios. Recently, deep learning–based methods for SSM have become predominant due to their outstanding performance. However, each sentence consists of a sequence of words, and it is usually viewed as one-dimensional ( 1D ) text, leading to the existing available neural models being restricted into 1D sequential networks. A few researches attempt to explore the potential of 2D or 3D neural models in text representation. However, it is hard for their works to capture the complex features in texts, and thus the achieved performance improvement is quite limited. To tackle this challenge, we devise a novel 3D CNN-based SSM ( 3DSSM ) method for human–robot language interaction. Specifically, first, a specific architecture called feature cube network is designed to transform a 1D sentence into a multi-dimensional representation named as semantic feature cube. Then, a 3D CNN module is employed to learn a semantic representation for the semantic feature cube by capturing both the local features embedded in word representations and the sequential information among successive words in a sentence. Given a pair of sentences, their representations are concatenated together to feed into another 3D CNN to capture the interactive features between them to generate the final matching representation. Finally, the semantic matching degree is judged with the sigmoid function by taking the learned matching representation as the input. Extensive experiments on two real-world datasets demonstrate that 3DSSM is able to achieve comparable or even better performance over the state-of-the-art competing methods.

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Losieva, Y. "Representation of Words in Natural Language Processing: A Survey." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 2 (2019): 82–87. http://dx.doi.org/10.17721/1812-5409.2019/2.10.

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The article is devoted to research to the state-of-art vector representation of words in natural language processing. Three main types of vector representation of a word are described, namely: static word embeddings, use of deep neural networks for word representation and dynamic) word embeddings based on the context of the text. This is a very actual and much-demanded area in natural language processing, computational linguistics and artificial intelligence at all. Proposed to consider several different models for vector representation of the word (or word embeddings), from the simplest (as a representation of text that describes the occurrence of words within a document or learning the relationship between a pair of words) to the multilayered neural networks and deep bidirectional transformers for language understanding, are described chronologically in relation to the appearance of models. Improvements regarding previous models are described, both the advantages and disadvantages of the presented models and in which cases or tasks it is better to use one or another model.

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BATURO, PAWEŁ, MARCIN PIATKOWSKI, and WOJCIECH RYTTER. "USEFULNESS OF DIRECTED ACYCLIC SUBWORD GRAPHS IN PROBLEMS RELATED TO STANDARD STURMIAN WORDS." International Journal of Foundations of Computer Science 20, no. 06 (December 2009): 1005–23. http://dx.doi.org/10.1142/s0129054109007017.

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The class of finite Sturmian words consists of words having particularly simple compressed representation, which is a generalization of the Fibonacci recurrence for Fibonacci words. The subword graphs of these words (especially their compacted versions) have a very special regular structure. In this paper we investigate this structure in more detail than in previous papers and show how several syntactical properties of Sturmian words follow from their graph properties. Consequently simple alternative graph-based proofs of several known facts are presented. The very special structure of subword graphs leads also to special easy algorithms computing some parameters of Sturmian words: the number of subwords, the critical factorization point, lexicographically maximal suffixes, occurrences of subwords of a fixed length, and right special factors. These algorithms work in linear time with respect to n, the size of the compressed representation of the standard word, though the words themselves can be of exponential size with respect to n. Some of the computed parameters can be also of exponential size, however we provide their linear size compressed representations. We introduce also a new concept related to standard words: Ostrowski automata.

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ÁLVAREZ-CAÑIZO, Marta, Paz SUÁREZ-COALLA, and Fernando CUETOS. "The role of sublexical variables in reading fluency development among Spanish children." Journal of Child Language 45, no. 4 (February 19, 2018): 858–77. http://dx.doi.org/10.1017/s0305000917000514.

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AbstractSeveral studies have found that, after repeated exposure to new words, children form orthographic representations that allow them to read those words faster and more fluently. However, these studies did not take into account variables related to the words. The aim of this study was to investigate the influence of sublexical variables on the formation of orthographic representations of words by Spanish children. The first experiment used pseudo-words of varying syllabic structure and syllabic frequency. The stimuli for the second experiment were formed with or without context-dependent graphemes. We found that formation of orthographic representations was influenced by syllabic structure (easier for words with simple syllabic structure) and the context-dependency of graphemes (easier in the absence of context-dependent graphemes), but not syllabic frequency. These results indicate that the easier it is to read a word, the easier it is to form an orthographic representation of it.

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Greenberg, Seth N., and Monika Nisslein. "Words do not stand alone: Do not ignore a word's role when examining patterns of activation." Behavioral and Brain Sciences 22, no. 2 (April 1999): 289–90. http://dx.doi.org/10.1017/s0140525x99301825.

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Pulvermüller traces the differences in brain activity associated with function and content words. The model considers words displayed primarily in isolation. Research on letter detection suggests that what distinguishes function from content words are their roles in text. Hence a model that fails to consider context effects on the processing of words provides an insufficient accounting of word representation in the brain.

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Soto, David, Usman Ayub Sheikh, Ning Mei, and Roberto Santana. "Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning." Royal Society Open Science 7, no. 5 (May 2020): 192043. http://dx.doi.org/10.1098/rsos.192043.

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How the brain representation of conceptual knowledge varies as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. In the present functional magnetic resonance imaging study, participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models—associated with the image referents of the words—and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control.

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